Can LLM Substitute Human Labeling? A Case Study of Fine-grained Chinese Address Entity Recognition Dataset for UAV Delivery
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Abstract (arXiv)
We present CNER-UAV, a fine-grained Chinese Name Entity Recognition dataset specifically designed for the task of address resolution in Unmanned Aerial Vehicle delivery systems. The dataset encompasses a diverse range of five categories, enabling comprehensive training and evaluation of NER models. To construct this dataset, we sourced the data from a real-world UAV delivery system and conducted a rigorous data cleaning and desensitization process to ensure privacy and data integrity. The resulting dataset, consisting of around 12,000 annotated samples, underwent human experts and Large Language Model annotation. We evaluated classical NER models on our dataset and provided in-depth analysis. The dataset and models are publicly available at urlhttps://github.com/zhhvvv/CNER-UAV.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 13 of its 23 references that are in the map. First posted on arXiv 10 Mar 2024.
Builds on
13 of 23 references.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 1.
5 papers.
- 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobilitycited by 49
- 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
- 2025Recent Advances in Transformer and Large Language Models for UAV Applicationscited by 3
- 2024Privacy in LLM-based Recommendation: Recent Advances and Future Directionscited by 2
- 2026A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languagescited by 0
- 2026A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages16 Sep 2026
- 2025Recent Advances in Transformer and Large Language Models for UAV Applications15 Aug 2025
- 2025UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility4 Jan 2025
- 2024Privacy in LLM-based Recommendation: Recent Advances and Future Directions3 Jun 2024
- 2024Large Language Models for UAVs: Current State and Pathways to the Future2 May 2024